Diploma ThesisDiploma Thesis

Image-Based Verification of Parametric Models in Heart-Ventricle Volumetry

Martin UrschlerInstitut für Maschinelles Sehen u. Darstellen

Techn. Universität Graz

In Zusammenarbeit mit

Prof. Rainer Rienmüller

Univ. Klinik f. Radiologie, LKH Graz

AgendaAgenda

Introduction Medical Image Data & Problems Volumetry

– Parametric Model (2-axis-method, Greene)– Segmentation-Based Models

Implementation– Overview– LiveWire Approach

Results Conclusion

IntroductionIntroduction

Goal: Measure volume of heart‘s left ventricle

Parametric vs. Segmentation-Based

Purpose: – Heart-Disease Diagnose

• stroke volume -> important function parameter

sliced heart left ventricle

Medical Image DataMedical Image Data

DICOM fileformat

10 Images per location (1 Heartbeat, ECG-triggered)

1 heartbeat

10 images

8 Long-Axis image locations

8 slices

Acquisition: Ultrafast CT Scanner

Example Image Data Set NKExample Image Data Set NK

Problems Problems

Partial Volume Effect Distinction between left ventricle

and surrounding tissue

gradient

Weak gradient information

Volumetry (I) - Parametric ModelVolumetry (I) - Parametric Model

Locate image with max. projected ventricle area

Calculate volume of modi-fied rotational ellipsoid

V = PI/6 * width * height^2

Measure ellipse parameters

width

height

Volumetry (II) - SegmentationVolumetry (II) - Segmentation

Basic Methods:– Thresholding– Edge Detecting Filters (Sobel, Canny)– Region Growing

Active Contours (Snakes) [Kass et al 88]

LiveWire [Barret92][Udupa,Falcao92]

Volume by Simpson Rule:– count segmented image pixels– multiply with voxel size

Implementation (II) - ThresholdingImplementation (II) - Thresholding

weak performance due to– partial volume, weak contrast, non-trivial

separation of chambers

Implementation (III) – SnakesImplementation (III) – Snakes

problems due to:– partial volume, weak

contrast– non-intuitive

parameterization, only possible after minimi-zation of contour

– outliers attracted to high gradients

– heavily depending on initial contour

Implementation (IV) - LiveWireImplementation (IV) - LiveWire

Seems to be very suitable for application!

Graph-theoretic, highly interactive

approach

LiveWire Approach (I)LiveWire Approach (I)

Segmentation consists of:– obj. recognition -> human better– obj. delineation -> machine/algorithm

better LiveWire combines human recognition

and automatic delineation!

LiveWire (II) - IngredientsLiveWire (II) - Ingredients

Image pixel -> node of graph

a

b

ce

d

cost(p,q) = w1*fz + w2*fg + w3*fd– p,q ... adjacent pixels (4- or 8-neighbours)– w1,w2,w3 ... weights– fz ... Laplacian Zero Crossing– fg ... Image gradient magnitude– fd ... Image gradient direction

cost(b,e)

cost(a,e)

cost(d,e)cost(c,e)

2 adjacent pixel -> directed arcs of graph– arcs are weighted by cost function

LiveWire (III) - AlgorithmLiveWire (III) - Algorithm

2 steps:1. Compute all shortest

paths in image to a selected start-point

2. While moving mouse, current position is end point -> select shortest path connecting start and end point

Find shortest paths -> Dijkstra

Start point

End pointEnd point

Shortest-Path map

LiveWire(V) - More FeaturesLiveWire(V) - More Features

Path cooling for intermed. pointsReal Time segmentation possible

(show demo!)

LiveWire Disadvantage:– Segmenting 16 images is faster than

manual segmentation but still time-consuming!

Results (I)Results (I)Evaluation of 31 data setsVolumes achieved by– Parametric model– Manually drawn contours (Prof. Rienmüller)– Thresholding– Contours after Snake segmentation– Contours after LiveWire segmentation

Results (II)Results (II)

LiveWire contours vs. parametric model

Similar results for Snake- and manually drawn contours

Results (III)Results (III) Comparison btw. LiveWire & manual contours

High correlation, fast & accurate reproduction of Prof. Rienmüller‘s contours!

Results (IV)Results (IV)

Summary & ConclusionSummary & Conclusion Comparison segmentation-based vs. parametric volume estimation Algorithms:

– Thresholding, Snakes– LiveWire

LiveWire shows excellent behaviour, it would be powerful for reducing segmentation time in the hands of a radiologist!

Future: 3D Region Growing?